survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling
Dublin Core
Title
survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling
Subject
survival analysis, health economic evaluation, probabilistic sensitivity analysis, R
Description
Survival analysis features heavily as an important part of health economic evaluation,
an increasingly important component of medical research. In this setting, it is important
to estimate the mean time to the survival endpoint using limited information (typically
from randomized trials) and thus it is useful to consider parametric survival models. In
this paper, we review the features of the R package survHE, specifically designed to wrap
several tools to perform survival analysis for economic evaluation. In particular, survHE
embeds both a standard, frequentist analysis (through the R package flexsurv) and a
Bayesian approach, based on Hamiltonian Monte Carlo (via the R package rstan) or integrated nested Laplace approximation (with the R package INLA). Using this composite
approach, we obtain maximum flexibility and are able to pre-compile a wide range of
parametric models, with a view of simplifying the modelers’ work and allowing them to
move away from non-optimal work flows, including spreadsheets (e.g., Microsoft Excel).
an increasingly important component of medical research. In this setting, it is important
to estimate the mean time to the survival endpoint using limited information (typically
from randomized trials) and thus it is useful to consider parametric survival models. In
this paper, we review the features of the R package survHE, specifically designed to wrap
several tools to perform survival analysis for economic evaluation. In particular, survHE
embeds both a standard, frequentist analysis (through the R package flexsurv) and a
Bayesian approach, based on Hamiltonian Monte Carlo (via the R package rstan) or integrated nested Laplace approximation (with the R package INLA). Using this composite
approach, we obtain maximum flexibility and are able to pre-compile a wide range of
parametric models, with a view of simplifying the modelers’ work and allowing them to
move away from non-optimal work flows, including spreadsheets (e.g., Microsoft Excel).
Creator
Gianluca Baio
Source
https://www.jstatsoft.org/article/view/v095i14
Publisher
University College London
Date
October 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Gianluca Baio, “survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling,” Repository Horizon University Indonesia, accessed April 11, 2025, https://repository.horizon.ac.id/items/show/8164.